Gene Clustering Algorithm Research Articles

A new gene tree algorithm employing DNA sequences of bovine genome using discrete Fourier transformation.

Within the realms of human thoughts on nature, Fourier analysis is considered as one of the greatest ideas currently put forwarded. The Fourier transform shows that any periodic function can be rewritten as the sum of sinusoidal functions. Having a Fourier transform view on real-world problems like the DNA sequence of genes, would make things intuitively simple to understand in comparison with their initial formal domain view. In this study we used discrete Fourier transform (DFT) on DNA sequences of a set of genes in the bovine genome known to govern milk production, in order to develop a new gene clustering algorithm. The implementation of this algorithm is very user-friendly and requires only simple routine mathematical operations. By transforming the configuration of gene sequences into frequency domain, we sought to elucidate important features and reveal hidden gene properties. This is biologically appealing since no information is lost via this transformation and we are therefore not reducing the number of degrees of freedom. The results from different clustering methods were integrated using evidence accumulation algorithms to provide in insilico validation of our results. We propose using candidate gene sequences accompanied by other genes of biologically unknown function. These will then be assigned some degree of relevant annotation by using our proposed algorithm. Current knowledge in biological gene clustering investigation is also lacking, and so DFT-based methods will help shine a light on use of these algorithms for biological insight.

Lineage-tracing and translatomic analysis of damage-inducible mitotic cochlear progenitors identifies candidate genes regulating regeneration.

Cochlear supporting cells (SCs) are glia-like cells critical for hearing function. In the neonatal cochlea, the greater epithelial ridge (GER) is a mitotically quiescent and transient organ, which has been shown to nonmitotically regenerate SCs. Here, we ablated Lgr5+ SCs using Lgr5-DTR mice and found mitotic regeneration of SCs by GER cells in vivo. With lineage tracing, we show that the GER houses progenitor cells that robustly divide and migrate into the organ of Corti to replenish ablated SCs. Regenerated SCs display coordinated calcium transients, markers of the SC subtype inner phalangeal cells, and survive in the mature cochlea. Via RiboTag, RNA-sequencing, and gene clustering algorithms, we reveal 11 distinct gene clusters comprising markers of the quiescent and damaged GER, and damage-responsive genes driving cell migration and mitotic regeneration. Together, our study characterizes GER cells as mitotic progenitors with regenerative potential and unveils their quiescent and damaged translatomes.

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

BackgroundRNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.ResultsThe input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.ConclusionsMBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Influence of the go-based semantic similarity measures in multi-objective gene clustering algorithm performance.

Using a prior biological knowledge of relationships and genetic functions for gene similarity, from repository such as the Gene Ontology (GO), has shown good results in multi-objective gene clustering algorithms. In this scenario and to obtain useful clustering results, it would be helpful to know which measure of biological similarity between genes should be employed to yield meaningful clusters that have both similar expression patterns (co-expression) and biological homogeneity. In this paper, we studied the influence of the four most used GO-based semantic similarity measures in the performance of a multi-objective gene clustering algorithm. We used four publicly available datasets and carried out comparative studies based on performance metrics for the multi-objective optimization field and clustering performance indexes. In most of the cases, using Jiang-Conrath and Wang similarities stand in terms of multi-objective metrics. In clustering properties, Resnik similarity allows to achieve the best values of compactness and separation and therefore of co-expression of groups of genes. Meanwhile, in biological homogeneity, the Wang similarity reports greater number of significant GO terms. However, statistical, visual, and biological significance tests showed that none of the GO-based semantic similarity measures stand out above the rest in order to significantly improve the performance of the multi-objective gene clustering algorithm.

An Enhanced Generalized Sequential Pattern Classification for Sequence Datasets

A Niche approach for classifying sequence Datasets is achieved by the use of EGSP (Enhanced Generalized sequential pattern) algorithm. The EGSP brings out a prediction model for working with the Sequence Datasets in the domain of Gene and Protein datasets. The Method proceeds by the way of generalizing the datasets of both the supervised and Semi-supervised data. The generalization brings out the candidate sequences which paves path for a distinct component extraction. The sequences are generated based on the threshold value which is then followed by applying the EGSP algorithm which brings out the sequential pattern from the pruned sequences. The extracted sequential pattern is then clustered using a gene clustering algorithm. The algorithm (MNBC) Modified Naïve Bayes Classification computes the probabilistic components for each class. The accuracy obtained is far better than the traditional classification algorithms. The resultant classification provides a solution for prediction methods for the selected domain and its applications. The algorithm used gives an upper hand over the computational costs which have been drastically minimized over the existing methods.

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

BackgroundBiologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence.MethodWe propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process.ResultsThe effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm.ConclusionsIntegrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.

A robust gene clustering algorithm based on clonal selection in multiobjective optimization framework

Gene clustering is a prerequisite in the analysis of microarray data where sets of co-expressed genes are clustered. In this paper, a multi-objective clonal selection optimization algorithm (MCSOA) is developed based on the immune system behavior for gene clustering purposes in which the number of clusters can vary in a predefined range. To achieve a reliable clustering outcome on various gene expression (GE) datasets, the most effective clustering validity indexes are incorporated and represented in terms of two conflicting objective functions. For the sake of fast convergence to the optimal solutions, a new population updating mechanism is iteratively applied to select the less-dominate solutions of the previous iteration. The proposed clustering technique is implemented on various publicly available microarray datasets. Comparing the results with those of the widely used gene clustering techniques confirms the superiority and efficacy of the proposed technique.

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering.

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

Extending the Arabidopsis flowering paradigm to a mass flowering phenomenon in the tropics

Flowering time is a critical life history trait, one that is shaped by evolution to maximize fecundity, reproductive success and fitness (Amasino 2010). This is especially true of annual plants where the cycle of floral initiation, pollination and seed production occur at regular intervals to ensure the survival of the species. In long-lived perennials, however, flowering can be an intermittent phenomenon and thus a challenge to understand. In this issue of Molecular Ecology, Kobayashi et al. (2013) tackle this particular challenge by applying modern-day molecular techniques to the ‘spectacular and mysterious’ mass flowering that takes places in mixed dipterocarp forests of South-East Asia. Here, amidst an almost unimaginable diversity of forbs, shrubs and trees, these authors used next-generation sequencing technology to characterize what they refer to as the ‘ecological transcriptome’ in an attempt to glimpse into the functional genomic reprogramming of Shorea beccariana at pre- and postflowering developmental transitions. They encountered many of the challenges that are often underappreciated yet typical for tropical ecological research including sample collection within a ~40-m high tree canopy, unpredictable flowering intervals and determining the most appropriate preflowering state for sampling. Despite these challenges, the authors were able to integrate gene ontology relationships with gene-clustering algorithms and environmental data to support the hypothesis that drought is a key trigger for flowering in S. beccariana. The cloning and transgenic expression of selected S. beccariana genes to corroborate presumed protein function is a key feature of their work and seldom applied within an ecological framework. As illustrated by Kobayashi et al. (2013), the inclusion of molecular biology, genomics and bioinformatics has the potential to shed light on long-standing questions of ecological concern.

Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data

Gene expression data clustering is one of the important tasks of functional genomics as it provides a powerful tool for studying functional relationships of genes in a biological process. Identifying coexpressed groups of genes represents the basic challenge in gene clustering problem. In this regard, a gene clustering algorithm, termed as robust rough-fuzzy c-means, is proposed judiciously integrating the merits of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition, the integration of probabilistic and possibilistic memberships of fuzzy sets enables efficient handling of overlapping partitions in noisy environment. The concept of possibilistic lower bound and probabilistic boundary of a cluster, introduced in robust rough-fuzzy c-means, enables efficient selection of gene clusters. An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on 14 yeast microarray data sets.

A new unsupervised gene clustering algorithm based on the integration of biological knowledge into expression data

BackgroundGene clustering algorithms are massively used by biologists when analysing omics data. Classical gene clustering strategies are based on the use of expression data only, directly as in Heatmaps, or indirectly as in clustering based on coexpression networks for instance. However, the classical strategies may not be sufficient to bring out all potential relationships amongst genes.ResultsWe propose a new unsupervised gene clustering algorithm based on the integration of external biological knowledge, such as Gene Ontology annotations, into expression data. We introduce a new distance between genes which consists in integrating biological knowledge into the analysis of expression data. Therefore, two genes are close if they have both similar expression profiles and similar functional profiles at once. Then a classical algorithm (e.g. K-means) is used to obtain gene clusters. In addition, we propose an automatic evaluation procedure of gene clusters. This procedure is based on two indicators which measure the global coexpression and biological homogeneity of gene clusters. They are associated with hypothesis testing which allows to complement each indicator with a p-value.Our clustering algorithm is compared to the Heatmap clustering and the clustering based on gene coexpression network, both on simulated and real data. In both cases, it outperforms the other methodologies as it provides the highest proportion of significantly coexpressed and biologically homogeneous gene clusters, which are good candidates for interpretation.ConclusionOur new clustering algorithm provides a higher proportion of good candidates for interpretation. Therefore, we expect the interpretation of these clusters to help biologists to formulate new hypothesis on the relationships amongst genes.

Relevant and Significant Supervised Gene Clusters for Microarray Cancer Classification

An important application of microarray data in functional genomics is to classify samples according to their gene expression profiles such as to classify cancer versus normal samples or to classify different types or subtypes of cancer. One of the major tasks with gene expression data is to find co-regulated gene groups whose collective expression is strongly associated with sample categories. In this regard, a gene clustering algorithm is proposed to group genes from microarray data. It directly incorporates the information of sample categories in the grouping process for finding groups of co-regulated genes with strong association to the sample categories, yielding a supervised gene clustering algorithm. The average expression of the genes from each cluster acts as its representative. Some significant representatives are taken to form the reduced feature set to build the classifiers for cancer classification. The mutual information is used to compute both gene-gene redundancy and gene-class relevance. The performance of the proposed method, along with a comparison with existing methods, is studied on six cancer microarray data sets using the predictive accuracy of naive Bayes classifier, K-nearest neighbor rule, and support vector machine. An important finding is that the proposed algorithm is shown to be effective for identifying biologically significant gene clusters with excellent predictive capability.

A dictionary‐based approach to normalizing gene names in one domain of knowledge from the biomedical literature

PurposeGene term variation is a shortcoming in text‐mining applications based on biomedical literature‐based knowledge discovery. The purpose of this paper is to propose a technique for normalizing gene names in biomedical literature.Design/methodology/approachUnder this proposal, the normalized forms can be characterized as a unique gene symbol, defined as the official symbol or normalized name. The unification method involves five stages: collection of the gene term, using the resources provided by the Entrez Gene database; encoding of gene‐naming terms in a table or binary matrix; design of a parametrized finite‐state graph (P‐FSG); automatic generation of a dictionary; and matching based on dictionary look‐up to transform the gene mentions into the corresponding unified form.FindingsThe findings show that the approach yields a high percentage of recall. Precision is only moderately high, basically due to ambiguity problems between gene‐naming terms and words and abbreviations in general English.Research limitations/implicationsThe major limitation of this study is that biomedical abstracts were analyzed instead of full‐text documents. The number of under‐normalization and over‐normalization errors is reduced considerably by limiting the realm of application to biomedical abstracts in a well‐defined domain.Practical implicationsThe system can be used for practical tasks in biomedical literature mining. Normalized gene terms can be used as input to literature‐based gene clustering algorithms, for identifying hidden gene‐to‐disease, gene‐to‐gene and gene‐to‐literature relationships.Originality/valueFew systems for gene term variation handling have been developed to date. The technique described performs gene name normalization by dictionary look‐up.

Mutual Information-Based Supervised Attribute Clustering for Microarray Sample Classification

Microarray technology is one of the important biotechnological means that allows to record the expression levels of thousands of genes simultaneously within a number of different samples. An important application of microarray gene expression data in functional genomics is to classify samples according to their gene expression profiles. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of coregulated genes whose collective expression is strongly associated with the sample categories or response variables. In this regard, a new supervised attribute clustering algorithm is proposed to find such groups of genes. It directly incorporates the information of sample categories into the attribute clustering process. A new quantitative measure, based on mutual information, is introduced that incorporates the information of sample categories to measure the similarity between attributes. The proposed supervised attribute clustering algorithm is based on measuring the similarity between attributes using the new quantitative measure, whereby redundancy among the attributes is removed. The clusters are then refined incrementally based on sample categories. The performance of the proposed algorithm is compared with that of existing supervised and unsupervised gene clustering and gene selection algorithms based on the class separability index and the predictive accuracy of naive bayes classifier, K-nearest neighbor rule, and support vector machine on three cancer and two arthritis microarray data sets. The biological significance of the generated clusters is interpreted using the gene ontology. An important finding is that the proposed supervised attribute clustering algorithm is shown to be effective for identifying biologically significant gene clusters with excellent predictive capability.

Algorithm for large-scale clustering across multiple genomes.

Identifying genomic regions that descended from a common ancestor helps us study the gene function and genome evolution. In distantly related genomes, clusters of homologous gene pairs are evidently used in function prediction, operon detection, etc. Currently, there are many kinds of computational methods that have been proposed defining gene clusters to identify gene families and operons. However, most of those algorithms are only available on a data set of small size. We developed an efficient gene clustering algorithm that can be applied on hundreds of genomes at the same time. This approach allows for large-scale study of evolutionary relationships of gene clusters and study of operon formation and destruction. An analysis of proposed algorithms shows that more biological insight can be obtained by analyzing gene clusters across hundreds of genomes, which can help us understand operon occurrences, gene orientations and gene rearrangements.

Comparative supragenomic analyses among the pathogens Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae Using a modification of the finite supragenome model

BackgroundStaphylococcus aureus is associated with a spectrum of symbiotic relationships with its human host from carriage to sepsis and is frequently associated with nosocomial and community-acquired infections, thus the differential gene content among strains is of interest.ResultsWe sequenced three clinical strains and combined these data with 13 publically available human isolates and one bovine strain for comparative genomic analyses. All genomes were annotated using RAST, and then their gene similarities and differences were delineated. Gene clustering yielded 3,155 orthologous gene clusters, of which 2,266 were core, 755 were distributed, and 134 were unique. Individual genomes contained between 2,524 and 2,648 genes. Gene-content comparisons among all possible S. aureus strain pairs (n = 136) revealed a mean difference of 296 genes and a maximum difference of 476 genes. We developed a revised version of our finite supragenome model to estimate the size of the S. aureus supragenome (3,221 genes, with 2,245 core genes), and compared it with those of Haemophilus influenzae and Streptococcus pneumoniae. There was excellent agreement between RAST's annotations and our CDS clustering procedure providing for high fidelity metabolomic subsystem analyses to extend our comparative genomic characterization of these strains.ConclusionsUsing a multi-species comparative supragenomic analysis enabled by an improved version of our finite supragenome model we provide data and an interpretation explaining the relatively larger core genome of S. aureus compared to other opportunistic nasopharyngeal pathogens. In addition, we provide independent validation for the efficiency and effectiveness of our orthologous gene clustering algorithm.

Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data

One of the major tasks with gene expression data is to find groups of coregulated genes whose collective expression is strongly associated with sample categories. In this regard, a new clustering algorithm, termed as fuzzy-rough supervised attribute clustering (FRSAC), is proposed to find such groups of genes. The proposed algorithm is based on the theory of fuzzy-rough sets, which directly incorporates the information of sample categories into the gene clustering process. A new quantitative measure is introduced based on fuzzy-rough sets that incorporates the information of sample categories to measure the similarity among genes. The proposed algorithm is based on measuring the similarity between genes using the new quantitative measure, whereby redundancy among the genes is removed. The clusters are refined incrementally based on sample categories. The effectiveness of the proposed FRSAC algorithm, along with a comparison with existing supervised and unsupervised gene selection and clustering algorithms, is demonstrated on six cancer and two arthritis data sets based on the class separability index and predictive accuracy of the naive Bayes' classifier, the K-nearest neighbor rule, and the support vector machine.

Gene networks involved in apoptosis induced by hyperthermia in human lymphoma U937 cells

To define the molecular mechanisms that mediate hyperthermia-induced apoptosis, we performed microarray and computational gene expression analyses. U937 cells, a human myelomonocytic lymphoma cell line, were treated with hyperthermia at 42 degrees C for 90 min and cultured at 37 degrees C. Apoptotic cells ( approximately 15%) were seen 6 h after hyperthermic treatment, and elevated expression of heat shock proteins (HSPs) including Hsp27, Hsp40, and Hsp70 was detected, following the activation of heat shock factor-1. Of the 54,675 probe sets analyzed, 1334 were upregulated and 4214 were downregulated by >2.0-fold in the cells treated with hyperthermia. A non-hierarchical gene clustering algorithm, K-means clustering, demonstrated 10 gene clusters. The gene network U1 or U2 that was obtained from up-regulated genes in cluster I or IX contained HSPA1B, DNAJB1, HSPH1, and TXN or PML, LYN, and DUSP1, and were mainly associated with cellular compromise, and cellular function and maintenance or death, and cancer, respectively. In the decreased gene cluster II, the gene network D1 including CCNE1 and CEBPE was associated with the cell cycle and cellular growth and proliferation. These findings will provide a basis for understanding the detailed molecular mechanisms of apoptosis induced by hyperthermia at 42 degrees C in cells.

Semi-supervised gene shaving method for predicting low variation biological pathways from genome-wide data.

BackgroundThe gene shaving algorithm and many other clustering algorithms identify gene clusters showing high variation across samples. However, gene expression in many signaling pathways show only modest and concordant changes that fail to be identified by these methods. The increasingly available signaling pathway prior knowledge provide new opportunity to solve this problem.ResultsWe propose an innovative semi-supervised gene clustering algorithm, where the original gene shaving algorithm was extended and generalized so that prior knowledge of signaling pathways can be incorporated. Different from other methods, our method identifies gene clusters showing concerted and modest expression variation as well as strong expression correlation. Using available pathway gene sets as prior knowledge, whether complete or incomplete, our algorithm is capable of forming tightly regulated gene clusters showing modest variation across samples. We demonstrate the advantages of our algorithm over the original gene shaving algorithm using two microarray data sets. The stability of the gene clusters was accessed using a jackknife approach.ConclusionOur algorithm represents one of the first clustering algorithms that is particularly designed to identify signaling pathways of low and concordant gene expression variation. The discriminating power is achieved by manufacturing a principal component enriched by signaling pathways.

Transcriptional Response of Human Umbilical Vein Endothelial Cells to Low Doses of Ionizing Radiation

We used cDNA microarray hybridization technology to monitor the transcriptional response of Human Umbilical Vein Endothelial (HUVEC) cells to x-rays doses ranging from 2 to 200 cGy. An early time window from irradiation (4h) was selected in order to minimize the effects of the cell cycle blockage eventually induced at high doses of irradiation. Three different gene-clustering algorithms have been used to group the 4134 monitored ORF based on their transcriptional response in function of the irradiation dose. The results show that while few genes exhibit a typical dose-dependent modulation with a variable threshold, most of them have a different modulation pattern, peaking at the two intermediate doses. Strikingly even the lowest dose used (2 cGy) seems to be very effective in transcriptional modulation. These results confirm the physiological relevance of sublethal-dose exposures of endothelial cells and strengthens the hypothesis that alternative dose-specific pathways of radioadaptive response exist in the mammalian cells. 111 genes were found to be modulated at all doses of irradiation. These genes were functionally classified by cellular process or by molecular function. Genes involved in coagulation and peroxidase activity and structural constituent of ribosomes were over-represented among the up-regulated genes as compared with their expected statistical occurrence. Three genes coding for regulatory kinase activities (CDK6; PRCKB1 and TIE) are found down-regulated at all doses of irradiation.